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Implementation of a Distributed Algorithm for Multi-camera Visual Feature Extraction in a Visual Sensor Network Testbed
KTH, School of Electrical Engineering (EES), Communication Networks.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Visual analysis tasks, like detection, recognition and tracking, are com-

putationally intensive, and it is therefore challenging to perform such tasks

in visual sensor networks, where nodes may be equipped with low power

CPUs. A promising solution is to augment the sensor network with pro-

cessing nodes, and to distribute the processing tasks among the process-

ing nodes of the visual sensor network. The objective of this project is

to enable a visual sensor network testbed to operate with multiple cam-

era sensors, and to implement an algorithm that computes the allocation

of the visual feature tasks to the processing nodes. In the implemented

system, the processing nodes can receive and process data from differ-

ent camera sensors simultaneously. The acquired images are divided into

sub-images, the sizes of the sub-images are computed through solving a

linear programming problem. The implemented algorithm performs local

optimization in each camera sensor without data exchange with the other

cameras in order to minimize the communication overhead and the data

computational load of the camera sensors. The implementation work is

performed on a testbed that consists of BeagleBone Black computers with

IEEE 802.15.4 or IEEE 802.11 USB modules, and the existing code base is

written in C++. The implementation is used to assess the performance of

the distributed algorithm in terms of completion time. The results show

a good performance providing lower average completion time.

Place, publisher, year, edition, pages
2015. , 64 p.
Series
EES Examensarbete / Master Thesis, XR-EE-LCN 2015:002
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-167415OAI: oai:DiVA.org:kth-167415DiVA: diva2:813267
Examiners
Available from: 2015-05-22 Created: 2015-05-22 Last updated: 2015-05-22Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • sv-SE
  • Other locale
More languages
Output format
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